By some estimates, 80% of a company's useful customer information is stored in unstructured data. This includes blogs, e-mails, forum posts, social media, and, increasingly, transcripts of phone calls. Each touch point with the customer results in potentially useful information about purchasing behaviors, lifestyles, interests, and the like. However, companies have not found a way to adequately capture this information and turn the data into useful action items.
There are a number of current art solutions that act as data aggregators or filters. These solutions are effective at enabling many representatives to contact customers, and generally monitor brand awareness. However, these solutions fall short in their ability to recognize and report the next best action to take with the customer.
With existing solutions, the content of the message is usually drafted by a customer service representative or the like. As such, there is no known way to automatically capture the attitudes and sentiment of the consumer towards various products. This is especially problematic at scale. First, companies with millions of customers cannot manually read every e-mail, social media post, etc. Second, humans may not be able to recognize less obvious shopping patterns and tendencies. Humans may fail to notice many buyer pattern correlations because they are often nonsensical or not apparent unless statistically derived. Accordingly, what is needed is a solution that solves at least one of the above-identified deficiencies.